Science, Health, and History: The Big Three of Misconceptions

These three domains account for 44% of all debunked myths

MakeoverMonday
Data Visualization
R Programming
2025
An analysis of 97 common misconceptions reveals that Science, Health, and History dominate the landscape of debunked myths.
Author

Steven Ponce

Published

July 7, 2025

Original

The original visualization Common MythConceptions comes from EInformation is Beutiful

Original visualization

Makeover

Figure 1: Treemap of 97 debunked misconceptions by category. Science, Health, and History are the three largest categories, accounting for 44% of all myths, with the remaining categories shown as smaller gray sections.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  scales,         # Scale Functions for Visualization
  glue,           # Interpreted String Literals
  lubridate,      # Make Dealing with Dates a Little Easier
  treemapify      # Draw Treemaps in 'ggplot2' 
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  10,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

myth_conceptions_raw<- read_csv(
  here::here('data/MakeoverMonday/2025/KIB - Common MythConceptions (public).csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(myth_conceptions_raw)
skimr::skim(myth_conceptions_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

# Helper
format_numbers_smart <- function(x) {
  case_when(
    x >= 1000000 ~ paste0(round(x / 1000000, 1), "M"),
    x >= 1000 ~ paste0(round(x / 1000, 0), "K"),
    x < 1000 & x > 0 ~ as.character(round(x, 0)),
    TRUE ~ "0"
  )
}

# Clean and prepare data
myths_clean <- myth_conceptions_raw |>
  rename(
    misconception = no_no_no_common_misconceptions,
    correction = remaining_text,
    search_volume = google_hits,
    noise_in_search = lot_of_noise_i_e_myth_busting_in_search_result
  ) |>
  filter(!is.na(misconception))

# Treemap data
treemap_data <- myths_clean |>
  filter(!is.na(category)) |>
  group_by(category) |>
  summarise(
    count = n(),
    total_hits = sum(search_volume, na.rm = TRUE),
    avg_hits = mean(search_volume, na.rm = TRUE),
    avg_word_count = mean(word_count, na.rm = TRUE),
    .groups = "drop"
  ) |>
  mutate(
    category_clean = case_when(
      category == "science" ~ "Science",
      category == "health" ~ "Health",
      category == "nature" ~ "Nature",
      category == "history" ~ "History",
      category == "physics" ~ "Physics",
      category == "religion" ~ "Religion",
      category == "cooking" ~ "Cooking",
      category == "mind" ~ "Psychology",
      category == "body" ~ "Human Body",
      category == "drugs" ~ "Substances",
      category == "food" ~ "Food & Nutrition",
      category == "technology - inventions" ~ "Technology",
      category == "science/nature" ~ "Science/Nature",
      TRUE ~ str_to_title(category)
    ),
    category_type = case_when(
      category == "science" ~ "Major: Science",
      category == "health" ~ "Major: Health",
      category == "history" ~ "Major: History",
      TRUE ~ "Minor Categories"
    ),
    label_text_hierarchical = paste0(
      toupper(category_clean), "\n",
      "\n",
      count, " myth", if_else(count == 1, "", "s"), "\n",
      format_numbers_smart(avg_hits), " avg searches"
    )
  ) |>
  filter(count > 0) |>
  arrange(desc(count))
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "Major: Science" = "#273F4F",
  "Major: Health" = "#447D9B", 
  "Major: History" = "#FE7743",
  "Minor Categories" = "#bababa"
))

### |-  titles and caption ----
title_text <- str_glue("Science, Health, and History: The Big Three of Misconceptions")
subtitle_text <- str_glue("These three domains account for **44%** of all debunked myths • Numbers show average Google searches per myth")

# Create caption
caption_text <- create_mm_caption(
  mm_year = 2025,
  mm_week = 29,
  source_text = "<br>EPI analysis of S&P Global (2024), IMPLAN (2024), and FRED data | Job-years, 2024-2032"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    
    # Legend formatting
    legend.position = "plot",
    legend.title = element_text(face = "bold"),
    
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 
    
    # Axis formatting
    axis.title.x = element_text(face = "bold", size = rel(0.85)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text.y = element_text(face = "bold", size = rel(0.85)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    
    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

# Plot ----
p <- ggplot(
  treemap_data,
  aes(
    area = count, fill = category_type,
    label = label_text_hierarchical
  )
) +
  # Geoms
  geom_treemap(
    color = "white",
    size = 2.5,
    alpha = 0.92
  ) +
  geom_treemap_text(
    family = "Arial",
    colour = "white",
    place = "centre",
    size = 11,
    fontface = "bold",
    min.size = 8,
    reflow = TRUE,
    padding.y = grid::unit(4, "mm"),
    padding.x = grid::unit(3, "mm")
  ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  coord_equal() +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
  ) +
  # Theme
  theme(
    plot.title = element_markdown(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      hjust = 0.5,
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9),
      hjust = 0.5,
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.9),
      lineheight = 0.9,
      margin = margin(t = 5, b = 20)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "makeovermonday", 
  year = 2025,
  week = 28,
  width = 10, 
  height = 10
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1       treemapify_2.5.6 glue_1.8.0       scales_1.3.0    
 [5] showtext_0.9-7   showtextdb_3.0   sysfonts_0.8.9   ggtext_0.1.2    
 [9] lubridate_1.9.3  forcats_1.0.0    stringr_1.5.1    dplyr_1.1.4     
[13] purrr_1.0.2      readr_2.1.5      tidyr_1.3.1      tibble_3.2.1    
[17] ggplot2_3.5.1    tidyverse_2.0.0  pacman_0.5.1    

loaded via a namespace (and not attached):
 [1] ggfittext_0.10.2  gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4
 [5] tzdb_0.5.0        vctrs_0.6.5       tools_4.4.0       generics_0.1.3   
 [9] curl_6.0.0        parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6      
[13] pkgconfig_2.0.3   skimr_2.1.5       lifecycle_1.0.4   farver_2.1.2     
[17] compiler_4.4.0    textshaping_0.4.0 munsell_0.5.1     repr_1.1.7       
[21] janitor_2.2.0     codetools_0.2-20  snakecase_0.11.1  htmltools_0.5.8.1
[25] yaml_2.3.10       crayon_1.5.3      pillar_1.9.0      camcorder_0.1.0  
[29] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[33] stringi_1.8.4     rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[37] grid_4.4.0        colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3   
[41] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       bit64_4.5.2      
[45] timechange_0.3.0  rmarkdown_2.29    bit_4.5.0         ragg_1.3.3       
[49] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[53] rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[57] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5      
[61] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_28.qmd.

For the full repository, click here.

10. References

  1. Data:
  1. Article
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